240 research outputs found
Quantum transport at the Dirac point: Mapping out the minimum conductivity from pristine to disordered graphene
The phase space for graphene's minimum conductivity is
mapped out using Landauer theory modified for scattering using Fermi's Golden
Rule, as well as the Non-Equilibrium Green's Function (NEGF) simulation with a
Monte Carlo sampling over impurity distributions. The resulting `fan diagram'
spans the range from ballistic to diffusive over varying aspect ratios (),
and bears several surprises. {The device aspect ratio determines how much
tunneling (between contacts) is allowed and becomes the dominant factor for the
evolution of from ballistic to diffusive regime. We find an
increasing (for ) or decreasing () trend in vs.
impurity density, all converging around at the dirty
limit}. In the diffusive limit, the {conductivity} quasi-saturates due to the
precise cancellation between the increase in conducting modes from charge
puddles vs the reduction in average transmission from scattering at the Dirac
Point. In the clean ballistic limit, the calculated conductivity of the lowest
mode shows a surprising absence of Fabry-P\'{e}rot oscillations, unlike other
materials including bilayer graphene. We argue that the lack of oscillations
even at low temperature is a signature of Klein tunneling
Dual-Stream Attention Transformers for Sewer Defect Classification
We propose a dual-stream multi-scale vision transformer (DS-MSHViT)
architecture that processes RGB and optical flow inputs for efficient sewer
defect classification. Unlike existing methods that combine the predictions of
two separate networks trained on each modality, we jointly train a single
network with two branches for RGB and motion. Our key idea is to use
self-attention regularization to harness the complementary strengths of the RGB
and motion streams. The motion stream alone struggles to generate accurate
attention maps, as motion images lack the rich visual features present in RGB
images. To facilitate this, we introduce an attention consistency loss between
the dual streams. By leveraging motion cues through a self-attention
regularizer, we align and enhance RGB attention maps, enabling the network to
concentrate on pertinent input regions. We evaluate our data on a public
dataset as well as cross-validate our model performance in a novel dataset. Our
method outperforms existing models that utilize either convolutional neural
networks (CNNs) or multi-scale hybrid vision transformers (MSHViTs) without
employing attention regularization between the two streams
Manifestation of chiral tunneling at a tilted graphene pn junction
Electrons in graphene follow unconventional trajectories at PN junctions,
driven by their pseudospintronic degree of freedom. Significant is the
prominent angular dependence of transmission, capturing the chiral nature of
the electrons and culminating in unit transmission at normal incidence (Klein
tunneling). We theoretically show that such chiral tunneling can be directly
observed from the junction resistance of a tilted interface probed with
separate split gates. The junction resistance is shown to increase with tilt in
agreement with recent experimental evidence. The tilt dependence arises because
of the misalignment between modal density and the anisotropic transmission lobe
oriented perpendicular to the tilt. A critical determinant is the presence of
edge scattering events that can completely reverse the angle-dependence. The
absence of such reversals in the experiments indicates that these edge effects
are not overwhelmingly deleterious, making the premise of transport governed by
electron `optics' in graphene an exciting possibility
Health Risk Assessment Associated with Norovirus Incidence in Raw Wastewater in Jeddah, Saudi Arabia
Abstract: Norovirus caused an epidemic gastroenteritis in humans. It can be transmitted by the fecaloral and the aerosol route. Norovirus represent a most common cause of acute gastroenteritis which responsible about 42%-96% of nonbacterial gastroenteritis worldwide. Current study aims to detect a norovirus in Jeddah wastewater. A total one hundred of wastewater samples were collected from outlet of Al-Misk Lake east of Jeddah city over a period of fourteen months from January 2009 to February 2010. All samples were filtered and virus concentrated and screened for GII human. A molecular inhouse detection was performed using nRT-PCR. The most conserved regions; N32, N33, N35 and N36 were used for primers design. Of 19 positive samples were signaled a band of 338bp. In conclusion, this study revealed that the norovirus was frequently present in Jeddah wastewater, which should be alert to do not use this water in land irrigation
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